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东张水库蓝藻水华BP人工神经网络模型演算研究 预览

Study on the calculus model of cyanobacterial bloom based on BP artificial neural network in Dongzhang Reservoir
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摘要 本文以福建省福清市东张水库为例,采集2016—2017年间包含水华期间在内的共295组的水质(水温、pH、电导率、浊度、溶解氧)和气象(气温、风速)数据,以80%的数据进行模型演算,20%的数据进行模型验证,以叶绿素a浓度作为输出参数,应用BP人工神经网络模型进行演算。通过输入不同的参数组合,将结果与实际测定的叶绿素a值比较,挑选出最优的参数组合。结果表明,当以水温、溶解氧、电导率和气温作为组合变量输入时,输出的结果最优,输出数据的预测值与实测值拟合度R^2为0.83,均方根误差(RMSE)为0.08μg/L,均方根-实测值标准偏差比(RSR)为0.43,且模型稳定性较好。表明该参数组合作为输入参数建立的BP人工神经网络预警模型,有望未来用于预测东张水库富营养化的发生。 This research took the Dongzhang Reservoir in Fuqing City,Fujian Province as an example,and collected water quality monitoring data(including water temperature,pH,conductivity,turbidity,and dissolved oxygen)and meteorology data(including air temperature,and wind speed)of 295 groups for the bloom period from 2016 to 2017.The BP artificial neural network model was set-up with 80% of the data set for model training,and 20% of the data set for model validation,while the concentration of chlorophyll-a was used as the output parameter.By testing different parameter combinations as input parameters,the results were compared with the measured chlorophyll-a concentrations in order to select the optimal parameter combination.The results showed that the combination of water temperature,dissolved oxygen,conductivity and air temperature were the optimal input parameter sets.Comparing with the measurements,the model performance was satisfied with the coefficient of determination(R^2 )equals to 0.83,the root mean square error(RMSE)equals to 0.08 μg/L,and the ratio of root mean square error to standard deviation(RSR)equals to 0.43.It was expected that the BP artificial neural network could be used to predict the occurrence of phytoplankton bloom in Dongzhang Reservoir as an early warning system.
作者 覃苗 张明峰 洪颐 苏玉萍 陈杨锋 李赫龙 陈宇昕 QIN Miao;ZHANG Minfeng;HONG Yi;SU Yuping;CHEN Yangfeng;LI Helong;CHEN Yuxin(Environmental Science and Engineering College,Fujian Normal University,Fuzhou 350007,China;Geographical Sciences College,Fujian Normal University,Fuzhou 350007,China;LEESU,MA 102,cole des Ponts Paris Tech,Agro Paris Tech,UPEC,UPE,77455 Champs-sur-Marne,France;Fujian Key Laboratory of Pollution Control and Resource Recycling,Fujian Normal University,Fuzhou 350007,China)
出处 《渔业研究》 2019年第1期18-25,共8页 Journal of Fujian Fisheries
基金 国家重点研发计划(2016YFE0202100) 国家自然科学基金(No.41573075) 福建省科技厅高校产学合作项目(2017Y4003) 福州市科技局项目(2016-G-68).
关键词 BP人工神经网络 叶绿素A 东张水库 蓝藻水华 BP artificial neural network chlorophyll-a Dongzhang Reservoir cyanobacteria bloom
作者简介 覃苗(1992-),女,湖北宜昌,硕士研究生,方向为水污染生态与预警模型应用研究.Tel:15871571801.E-mail:444356899@qq.com;通讯作者:苏玉萍(1972-),女,教授,从事水污染生态与修复方面研究.Tel:17706929985.E-mail:ypsu@fjnu.edu.cn.
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